124 citations · 528 across the 41 of their papers we have counts for
8 papers · 1 filter
Dual Parameterization of Sparse Variational Gaussian Processes
Vincent Adam, Paul E. Chang, Mohammad Emtiyaz Khan +1
Sparse variational Gaussian process (SVGP) methods are a common choice for non-conjugate Gaussian process inference because of their computational benefits. In this paper, we impro…
Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression
Ayush Jain, P. K. Srijith, Mohammad Emtiyaz Khan
Deep Gaussian Processes (DGPs) are multi-layer, flexible extensions of Gaussian processes but their training remains challenging. Sparse approximations simplify the training but of…
Structured second-order methods via natural gradient descent
Wu Lin, Frank Nielsen, Mohammad Emtiyaz Khan +1
In this paper, we propose new structured second-order methods and structured adaptive-gradient methods obtained by performing natural-gradient descent on structured parameter space…
The Bayesian Learning Rule
Mohammad Emtiyaz Khan, Håvard Rue
We show that many machine-learning algorithms are specific instances of a single algorithm called the \emph{Bayesian learning rule}. The rule, derived from Bayesian principles, yie…
Knowledge-Adaptation Priors
Mohammad Emtiyaz Khan, Siddharth Swaroop
Humans and animals have a natural ability to quickly adapt to their surroundings, but machine-learning models, when subjected to changes, often require a complete retraining from s…
Bridging the Gap Between Target Networks and Functional Regularization
Alexandre Piché, Valentin Thomas, Rafael Pardinas +4
Bootstrapping is behind much of the successes of deep Reinforcement Learning. However, learning the value function via bootstrapping often leads to unstable training due to fast-ch…